Anomaly Prediction for Electronic Resources

4. The computer-implemented method of claim 3, wherein the machine learning model is trained using Gaussian prediction model parameters and a quantity of an anomalous event predicted for at least one future time or date.

5. The computer-implemented method of claim 1, wherein the set of distribution parameters includes at least mean, standard deviation, and density.

7. The computer-implemented method of claim 6, wherein the normal distribution and the anomaly distribution are determined using a Gaussian mixture model.

9. The computer-implemented method of claim 6, wherein an additional distribution is determined from the historical time series data, and wherein the anomaly distribution is determined from the normal distribution and the additional distribution, the additional distribution including a Gaussian distribution or a binomial distribution.

12. The computer-implemented method of claim 6, wherein the prediction of the future occurrence is further determined using a framework that provides for feature engineering along with the time-series forecasting model.

13. The computer-implemented method of claim 12, wherein the machine learning model is trained using Gaussian prediction model parameters and a quantity of an anomalous event predicted for at least one future time or date.

14. The computer-implemented method of claim 6, wherein the set of distribution parameters includes at least mean, standard deviation, and density.

17. The system of claim 16, wherein the event distribution and the anomaly distribution are determined using a Gaussian mixture model.

20. The system of claim 16, wherein the prediction of the future occurrence is further determined using a framework that provides for feature engineering along with the time-series forecasting model, and wherein the machine learning model is trained using Gaussian prediction model parameters and a quantity of an anomalous event predicted for at least one future time or date.